Prevalence and Factors Associated with Road Traffic Crash among Taxi Drivers in Mekelle Town,Northern Ethiopia, 2014: A Cross Sectional Study

Objectives

The 2013 World Health Organization Status Report on Road Safety estimated that approximately 1.24 million deaths occur annually due to road traffic crashes with most of the burden falling on low- and middle-income countries. The objective of this research is to study the prevalence of road traffic crashes in Mekelle, Tigray, Northern Ethiopia and to identify risk factors with the ultimate goal of informing prevention activities and policies.

Methods

This study used a cross-sectional design to measure the prevalence and factors associated with road traffic crashes among 4-wheeled minibus (n = 130) and 3-wheeled Bajaj (n = 582) taxi drivers in Mekelle, Ethiopia. Bivariate and multivariate logistic regression were used to evaluate the association between risk factors and drivers’ involvement in a road traffic crash within the 3 years prior to the survey.

Findings

Among the 712 taxi drivers, 26.4% (n = 188) of them reported involvement in a road traffic crash within the past 3 years. Drivers who listened to mass media had decreased likelihood of road traffic crash involvement (AOR = 0.51, 0.33–0.78), while speedy driving (AOR = 4.57, 3.05–7.44), receipt of a prior traffic punishment (AOR = 4.57, 2.67–7.85), and driving a mechanically faulty taxi (AOR = 4.91, 2.81–8.61) were strongly associated with road traffic crash involvement. Receiving mobile phone calls while driving (AOR = 1.91, 1.24–2.92) and history of alcohol use (AOR = 1.51, 1.00–2.28) were also associated with higher odds of road traffic crash involvement.

Conclusion

The results of this study show that taxi drivers in Mekelle habitually place themselves at increased risk of road traffic crashes by violating traffic laws, especially related to speedy driving, mobile phone use, and taxi maintenance. This research can be used to support re-evaluation of the type, severity, and enforcement of traffic violation penalties.     

Driving following Kava Use and Road Traffic Injuries: A Population-Based Case-Control Study in Fiji (TRIP 14)

Objective

To investigate the association between kava use and the risk of four-wheeled motor vehicle crashes in Fiji. Kava is a traditional beverage commonly consumed in many Pacific Island Countries. Herbal anxiolytics containing smaller doses of kava are more widely available.

Methods

Data for this population-based case-control study were collected from drivers of ‘case’ vehicles involved in serious injury-involved crashes (where at least one road user was killed or admitted to hospital for 12 hours or more) and ‘control’ vehicles representative of ‘driving time’ in the study base. Structured interviewer administered questionnaires collected self-reported participant data on demographic characteristics and a range of risk factors including kava use and potential confounders. Unconditional logistic regression models estimated odds ratios relating to the association between kava use and injury-involved crash risk.

Findings

Overall, 23% and 4% of drivers of case and control vehicles, respectively, reported consuming kava in the 12 hours prior to the crash or road survey. After controlling for assessed confounders, driving following kava use was associated with a four-fold increase in the odds of crash involvement (Odds ratio: 4.70; 95% CI: 1.90–11.63). The related population attributable risk was 18.37% (95% CI: 13.77–22.72). Acknowledging limited statistical power, we did not find a significant interaction in this association with concurrent alcohol use.

Conclusion

In this study conducted in a setting where recreational kava consumption is common, driving following the use of kava was associated with a significant excess of serious-injury involved road crashes. The precautionary principle would suggest road safety strategies should explicitly recommend avoiding driving following kava use, particularly in communities where recreational use is common.     

Comorbidities and Crash Involvement among Younger and Older Drivers

Previous studies identified comorbidities as predictors of older driver performance and driving pattern, while the direct impact of comorbidities on road crash risk in elderly drivers is still unknown. The present study is a cross-sectional aimed at investigating the association between levels of comorbidity and crash involvement in adult and elderly drivers. 327 drivers were stratified according to age range in two groups: elderly drivers (age ≥70 years old, referred as older) and adult drivers (age <70 years old, referred as younger). Driving information was obtained through a driving questionnaire. Distance traveled was categorized into low, medium and high on the basis of kilometers driven in a year. CIRS-illness severity (IS) and CIRS-comorbidity indices (CI) in all populations were calculated. Older drivers had a significantly higher crash involvements rate (p = .045) compared with the younger group based on the number of licensed drivers. Dividing comorbidity indices into tertiles among all licensed subjects, the number of current drivers significantly decreased (p<.0001) with increasing level of comorbidity. The number of current drivers among older subjects significantly decreased with increasing comorbidity level (p = .026) while no difference among younger group was found (p = .462). Among younger drivers with increasing comorbidity level, the number of road accidents significantly increased (p = .048) and the logistic regression analysis showed that comorbidity level significantly associated with crash involvement independent of gender and driving exposure. Older subjects with high level of comorbidity are able to self-regulate driving while comorbidity burden represents a significant risk factor for crash involvements among younger drivers.     

Driving Performance on the Descending Limb of Blood Alcohol Concentration (BAC) in Undergraduate Students: A Pilot Study

Young drivers are overrepresented in collisions resulting in fatalities. It is not uncommon for young drivers to socially binge drink and decide to drive a vehicle a few hours after consumption. To better understand the risks that may be associated with this behaviour, the present study has examined the effects of a social drinking bout followed by a simulated drive in undergraduate students on the descending limb of their BAC (blood alcohol concentration) curve. Two groups of eight undergraduate students (n = 16) took part in this study. Participants in the alcohol group were assessed before drinking, then at moderate and low BAC as well as 24 hours post-acute consumption. This group consumed an average of 5.3 ± 1.4 (mean ± SD) drinks in an hour in a social context and were then submitted to a driving and a predicted crash risk assessment. The control group was assessed at the same time points without alcohol intake or social context.; at 8 a.m., noon, 3 p.m. and 8 a.m. the next morning. These multiple time points were used to measure any potential learning effects from the assessment tools (i.e. driving simulator and useful field of view test (UFOV)). Diminished driving performance at moderate BAC was observed with no increases in predicted crash risk. Moderate correlations between driving variables were observed. No association exists between driving variables and UFOV variables. The control group improved measures of selective attention after the third asessement. No learning effect was observed from multiple sessions with the driving simulator. Our results show that a moderate BAC, although legal, increases the risky behaviour. Effects of alcohol expectancy could have been displayed by the experimental group. UFOV measures and predicted crash risk categories were not sentitive enough to predict crash risk for young drivers, even when intoxicated.     

Current Analogues of Future Climate Indicate the Likely Response of a Sensitive Montane Tropical Avifauna to a Warming World

Among birds, tropical montane species are likely to be among the most vulnerable to climate change, yet little is known about how climate drives their distributions, nor how to predict their likely responses to temperature increases. Correlative models of species’ environmental niches have been widely used to predict changes in distribution, but direct tests of the relationship between key variables, such as temperature, and species’ actual distributions are few. In the absence of historical data with which to compare observations and detect shifts, space-for-time substitutions, where warmer locations are used as analogues of future conditions, offer an opportunity to test for species’ responses to climate. We collected density data for rainforest birds across elevational gradients in northern and southern subregions within the Australian Wet Tropics (AWT). Using environmental optima calculated from elevational density profiles, we detected a significant elevational difference between the two regions in ten of 26 species. More species showed a positive (19 spp.) than negative (7 spp.) displacement, with a median difference of ∼80.6 m across the species analysed that is concordant with that expected due to latitudinal temperature differences (∼75.5 m). Models of temperature gradients derived from broad-scale climate surfaces showed comparable performance to those based on in-situ measurements, suggesting the former is sufficient for modeling impacts. These findings not only confirm temperature as an important factor driving elevational distributions of these species, but also suggest species will shift upslope to track their preferred environmental conditions. Our approach uses optima calculated from elevational density profiles, offering a data-efficient alternative to distribution limits for gauging climate constraints, and is sensitive enough to detect distribution shifts in this avifauna in response to temperature changes of as little as 0.4 degrees. We foresee important applications in the urgent task of detecting and monitoring impacts of climate change on montane tropical biodiversity.     

How Does the Driver’s Perception Reaction Time Affect the Performances of Crash Surrogate Measures?

With the merit on representing traffic conflict through examining the crash mechanism and causality proactively, crash surrogate measures have long been proposed and applied to evaluate the traffic safety. However, the driver’s Perception-Reaction Time (PRT), an important variable in crash mechanism, has not been considered widely into surrogate measures. In this regard, it is important to know how the PRT affects the performances of surrogate indicators. To this end, three widely used surrogate measures are firstly modified by involving the PRT into their crash mechanisms. Then, in order to examine the difference caused by the PRT, a comparative study is carried out on a freeway section of the Pacific Motorway, Australia. This result suggests that the surrogate indicators’ performances in representing rear-end crash risks are improved with the incorporating of the PRT for the investigated section.     

Improving the Health Forecasting Alert System for Cold Weather and Heat-Waves In England: A Proof-of-Concept Using Temperature-Mortality Relationships

ObjectivesIn this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office’s (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings.MethodThe prototype health forecasting alert system introduces an “impact vs likelihood matrix” for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system.ConclusionsThe prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use.     

Climatic influences on the mortality of sheep during long-distance sea transport

Research on the causes of sheep death in sea voyages from Australia to the Middle East is limited, in particular little is known about the influence of climatic factors. Mortality data from 417 shipments of sheep exported over an 11-year period (November 2004 to June 2015) were modelled retrospectively to determine associated climatic factors. The statistical analysis were performed for both the full data set with 417 voyages based on actual and estimated departure and arrival dates and a restricted data set with 71 voyages based on actual dates. The results of the full data set demonstrated a seasonal mortality pattern, with more deaths occurring on sea voyages leaving Australia in the southern hemisphere winter or spring than those departing in Australian summer or autumn. Heat stress and inadequate fat mobilisation for energy supply when sheep are inappetant on shipments may explain this seasonality. Based on these two models, the voyage and weather factors associated with sheep mortalities included departure year, autumn departure in the southern hemisphere, voyage duration, single or multiple loading port(s), weekly mean dry bulb temperature and wind speed at departure ports, and humidity at destination ports. Significant correlations were observed between weather variables at the departure ports in the Australian winter and a high sheep mortality rate during voyages. This, together with the anticipated increased heat stress risk as a result of climate change, suggests that there could be review of the trade from Australia in the southern hemisphere winter. The influence of weather at the departure ports should be considered in sheep mortality prediction models, especially Australia’s heat stress risk assessment model.     

Driver sleepiness and risk of serious injury to car occupants: population based case control study

ObjectivesTo estimate the contribution of driver sleepiness to the causes of car crash injuries.DesignPopulation based case control study.SettingAuckland region of New Zealand, April 1998 to July 1999.Participants571 car drivers involved in crashes where at least one occupant was admitted to hospital or killed (“injury crash”); 588 car drivers recruited while driving on public roads (controls), representative of all time spent driving in the study region during the study period.ResultsThere was a strong association between measures of acute sleepiness and the risk of an injury crash. After adjustment for major confounders significantly increased risk was associated with drivers who identified themselves as sleepy (Stanford sleepiness score 4-7 v 1-3; odds ratio 8.2, 95% confidence interval 3.4 to 19.7); with drivers who reported five hours or less of sleep in the previous 24 hours compared with more than five hours (2.7, 1.4 to 5.4); and with driving between 2 am and 5 am compared with other times of day (5.6, 1.4 to 22.7). No increase in risk was associated with measures of chronic sleepiness. The population attributable risk for driving with one or more of the acute sleepiness risk factors was 19% (15% to 25%).ConclusionsAcute sleepiness in car drivers significantly increases the risk of a crash in which a car occupant is injured or killed. Reductions in road traffic injuries may be achieved if fewer people drive when they are sleepy or have been deprived of sleep or drive between 2 am and 5 am.

What is already known on this topic

Driver sleepiness is considered a potentially important risk factor for car crashes and related injuries but the association has not been reliably quantifiedPublished estimates of the proportion of car crashes attributable to driver sleepiness vary from about 3% to 30%

What this study adds

Driving while feeling sleepy, driving after five hours or less of sleep, and driving between 2 am and 5 am were associated with a substantial increase in the risk of a car crash resulting in serious injury or deathReduction in the prevalence of these three behaviours may reduce the incidence of injury crashes by up to 19%     

Human impacts and the global distribution of extinction risk

Understanding the global geographical distribution of extinction risk is a key challenge in conservation biology. It remains controversial, however, to what extent areas become threat hotspots simply because of high human impacts or due to predisposing ecological conditions. Limits to the taxonomic and geographical extent, resolution and quality of previously available data have precluded a full global assessment of the relative roles of these factors. Here, we use a new global database on the geographical distributions of birds on continents and continental islands to show that, after controlling for species richness, the best predictors of the global pattern of extinction risk are measures of human impact. Ecological gradients are of secondary importance at a global scale. The converse is true for individual biogeographic realms, within which variation in human impact is reduced and its influence on extinction risk globally is therefore underestimated. These results underline the importance of a global perspective on the mechanisms driving spatial patterns of extinction risk, and the key role of anthropogenic factors in driving the current extinction crisis.     

Variable impact transportation (VIT) model for energy and environmental impact of hauling truck operation

Purpose

Fuel economy and emissions of heavy-duty trucks greatly vary based on vehicular/environmental conditions. Large-scale infrastructure construction projects require a large amount of material/equipment transportation. Single-parameter generic hauling models may not be the best option for an accurate estimation of hauling contribution in life cycle assessment (LCA) involving construction projects; therefore, more precise data and parameterized models are required to represent this contribution. This paper discusses key environmental/operational variables and their impact on transportation of materials and equipment; a variable-impact transportation (VIT) model accounting for these variables was developed to predict environmental impacts of hauling.

Methods

The VIT model in the form of multi-nonlinear regression equations was developed based on simulations using the U.S. Environmental Protection Agency (EPA)’s Motor Vehicle Emission Simulator (MOVES) to compute all the impact categories in EPA’s TRACI 2.1 and energy consumption of transportation. Considering actual driving cycles of hauling trucks recorded during a pavement rehabilitation project, the corresponding environmental impacts were calculated, and sensitivity analyses were performed. In addition, an LCA case study based on historical pavement reconstruction projects in Illinois was conducted to analyze the contribution of transportation and variability of its impacts during the pavements’ life cycle.

Results and discussion

The importance of vehicle driving cycles was realized from simulation results. The case study results showed that considering driving cycles using the VIT model could increase the contribution of hauling in total life cycle Global Warming Potential (GWP) and total life cycle GWP itself by 2–4 and 3–5%, respectively. In addition to GWP, ranges of other hauling-related impact categories including Smog, Ozone Depletion, Acidification, and Primary Energy Demand from fuel were presented based on the case study. Ozone Depletion ranged from 9 to 45%, and Smog ranged from 11 to 48% of the total relevant life cycle impacts. The GWP contribution of hauling in pavement LCA ranged between 5 and 32%. The results indicate that the contribution of hauling transportation can be significant in pavement LCA.

Conclusions

For large-scale roadway infrastructure construction projects that need a massive amount of material transportation, high fidelity models and data should be used especially for comparative LCAs that can be used as part of decision making between alternatives. The VIT model provides a simple analytical platform to include the critical vehicular/operational variables without any dependence on an external software; the model can also be incorporated in those studies where some of the transportation activity data are available.

     

Construction of a risk model through the fusion of experimental data and finite element modeling: Application to car crash-induced TBI

This article introduces a new approach for the construction of a risk model for the prediction of Traumatic Brain Injury (TBI) as a result of a car crash. The probability of TBI is assessed through the fusion of an experiment-based logistic regression risk model and a finite element (FE) simulation-based risk model. The proposed approach uses a multilevel framework which includes FE simulations of vehicle crashes with dummy and FE simulations of the human brain. The loading conditions derived from the crash simulations are transferred to the brain model thus allowing the calculation of injury metrics such as the Cumulative Strain Damage Measure (CSDM). The framework is used to propagate uncertainties and obtain probabilities of TBI based on the CSDM injury metric. The risk model from FE simulations is constructed from a support vector machine classifier, adaptive sampling, and Monte-Carlo simulations. An approach to compute the total probability of TBI, which combines the FE-based risk assessment as well as the risk prediction from the experiment-based logistic regression model is proposed. In contrast to previous published work, the proposed methodology includes the uncertainty of explicit parameters such as impact conditions (e.g., velocity, impact angle), and material properties of the brain model. This risk model can provide, for instance, the probability of TBI for a given assumed crash impact velocity.     

Monitoring trends in the extent of major floods in the lower reach of Songkhram River Basin, Northeastern Thailand

This study analyzes trends in the extent of major floods in the lower reach of Songkhram River Basin, one of the most important areas for aquatic biological production in the Lower Mekong River Basin. We first classified the time series Landsat imagery acquired at a 2-year interval between 2000 and 2006 using the unsupervised classification method. We then analyzed the extent of major floods through the image matrix analysis on seasonal land cover map pairs. Additionally, we estimated flood volumes and discharge rates for each time series. We finally analyzed the extent of land uses that were affected by major flood events. Accuracy assessment showed that the extent of major floods was accurately mapped. Analysis of trends of major floods revealed that there was a considerable variation in the extent through 2006, with the flood decreasing since 2002. Analysis of flood risk areas based on the 2000–2004 data showed that about 3.04% of the study areas was at high risk of being flooded. Between 2000 and 2006, about 5.5% of the study area that are classified as agriculture and built-up land uses was affected by major floods. Our estimates on flood volumes and discharge rates are consistent with the data from other studies. Overall findings suggest that accurate mapping of major floods and flood risk areas using space and time dependent data can be important for developing protocols for flash flood early warning and flood risk management and mitigation. Output GIS maps and data combined with crude assumptions about water flow will serve as the baseline data to estimate the amount of water that flows in and out of the basin. Further research should focus on integration of social science research to evaluate the socio-economic impacts of major floods and identifying coping strategies of affected communities.     

Primary School Teachers’ Assessment Profiles in Mathematics Education

The aim of this study was to contribute to knowledge about classroom assessment by identifying profiles of teachers’ assessment of their students’ understanding of mathematics. For carrying out this study we used data of a nationwide teacher survey (N = 960) in the Netherlands. The data were collected by an online questionnaire. Through exploratory factor analyses the underlying structure of what is measured by this questionnaire was uncovered as consisting of five factors: Goal centeredness of assessment, Authentic nature of assessment, Perceived usefulness of assessment, Diversity of assessment problem format, and Allocated importance of assessing skills and knowledge. By using a latent class analysis four different assessment profiles of teachers were identified: Enthusiastic assessors, Mainstream assessors, Non-enthusiastic assessors, and Alternative assessors. The findings suggest that teachers with particular assessment profiles have qualitatively different assessment practices. The paper concludes with discussing theoretical implications of these assessment profiles and indications these profiles can offer both for designing material for professional development in classroom assessment and for evaluating changes in teachers’ classroom assessment practice.     

Quantitative microbiological risk assessment: principles applied to determining the comparative risk of salmonellosis from chicken products

Since causal links have been established between food-borne illness and particular microorganisms, it has been possible to assess the public health risks of their presence in foods and propose measures to ensure the safety of customers. Effective control measures for food safety have been based on knowledge of the resistance of pathogenic microorganisms to the treatments used for preservation (e.g. acidification or reduced water activity) or decontamination (e.g. pasteurisation or sterilisation). Using this principle, food safety has been managed informally and successfully within the food industry for many years. Recently, formal risk assessment schemes, for example from Codex Alimentarius, have developed and placed the elements of decision-making on suitable control measures into a formal framework with clearly identifiable stages. The output of the four stages of a formal risk assessment (hazard identification, hazard characterisation, exposure assessment and risk characterisation) provides the basis for decisions on actions needed to control the identified hazard. There are many difficulties in ensuring that risk assessments are realistic and accessible to potential users, in many cases their value is limited by the data available. The study reported here on control of Salmonella in poultry products illustrates that it is possible to produce a comparative risk assessment based on published data. This study is not a full quantitative risk assessment, but provide useful pointers for a risk manager. Differences are discussed between the exposure assessment data needed to propose controls for infectious or toxigenic pathogens. For infectious pathogens, the presence of viable and infectious microorganisms is itself the hazard, but for toxigenic microorganisms, absence or destruction of viable cells at ingestion does not in itself ensure the absence of toxin. For toxin hazards, exposure assessment needs to consider previous conditions that may have led to toxin formation and persistence, rather than just the level of microbes at ingestion.     

Analyzing the Long Term Cohesive Effect of Sector Specific Driving Forces

Financial markets are partially composed of sectors dominated by external driving forces, such as commodity prices, infrastructure and other indices. We characterize the statistical properties of such sectors and present a novel model for the coupling of the stock prices and their dominating driving forces, inspired by mean reverting stochastic processes. Using the model we were able to explain the market sectors’ long term behavior and estimate the coupling strength between stocks in financial markets and the sector specific driving forces. Notably, the analysis was successfully applied to the shipping market, in which the Baltic dry index (BDI), an assessment of the price of transporting the major raw materials by sea, influences the shipping financial market. We also present the analysis of other sectors—the gold mining market and the food production market, for which the model was also successfully applied. The model can serve as a general tool for characterizing the coupling between external forces and affected financial variables and therefore for estimating the risk in sectors and their vulnerability to external stress.     

Extreme weather conditions correspond with localised vegetation death at Cradle Mountain,Tasmania

A chance observation of a drought‐related plant mortality event in early 2014 in a normally wet and cool alpine area was matched with local weather data providing a unique insight into this event. The observed plant death was largely indiscriminate in areas that were topographically predisposed to being susceptible to drought. The weather conditions surrounding this event included 5 weeks with very little rain, an extreme heatwave and subsequent brief periods where warm temperatures and dry air combined to produce highly evaporative conditions. Extreme weather conditions such as this are expected to occur with increasing frequency as a result of climate change. Observing and reporting on real‐world examples of how extreme weather events affect native vegetation is integral to improved climate change risk assessment and to inform future management actions.